Abstract 17970: Atrial Fibrillation is Associated With Increased Atrial Metabolic Activity on FDG/PET Imaging in Patients With Ischemic Cardiomyopathy
Bibliographic record
Abstract
Introduction: Atrial Fibrillation (AF) is a common arrhythmia but its pathogenesis remains incompletely understood. Alterations in metabolism may play a role. 18F-fluorodeoxyglucose (FDG) PET reflects glucose uptake in metabolically active tissue but studies evaluating the relationship between atrial FDG uptake and atrial fibrillation are limited. Hypothesis: We hypothesized that patients with ischemic cardiomyopathy who have had AF have increased atrial wall FDG uptake compared to patients without AF (no-AF). Methods: Atrial uptake was assessed visually and quantitatively on FDG-PET/CT images in 42 patients (24 AF, 18 no-AF). The maximum and mean FDG Standard Uptake Value (SUV) in the left atrium (LA), right atrium (RA) and mean blood pool activity were measured (see figure). The Tissue:Blood ratio (TBR) was calculated using the maximum atrial wall SUV (TBRmax) and mean SUV (TBRmean). Results: Median age was 72 years; (78% male). Mean LVEF was similar between AF vs. No-AF patients (24+/-10% vs. 26+/-11%, p=0.57). The RA was visualized in 88% of AF and 44% of no-AF patients (p=0.002). The LA was visualized in 88% of AF and 72% of no-AF patients (p=0.22). See table for TBR results. TBRmax and TBRmean were significantly increased in the RA wall of patients with AF vs. no-AF. There was a trend for increased FDG in the LA wall of AF patients. Conclusion: Among patients with ischemic cardiomyopathy, significantly greater FDG uptake was noted in the RA of patients with AF vs. No-AF (with a trend for LA uptake). These findings suggest increased glucose metabolism in the atria in patients with AF. Further study is warranted to determine the clinical and biological significance of these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".